Demis Hassabis, Ph.D.

Pioneer of Artificial Intelligence

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Founder and CEO, DeepMind Technologies

Date of Birth

July 27, 1976

Demis Hassabis, the eldest of three, was born in London on July 27, 1976, to a Greek Cypriot father and a Chinese Singaporean mother.

Demis Hassabis was born in London, England. He is of both Greek and Chinese ancestry; his father came from Cyprus, his mother from Singapore. Demis and his family moved frequently as his father pursued a variety of business and creative ventures. Demis was four years old when he saw his father and an uncle playing chess and asked them to teach him the game. He took to it quickly and was soon beating both of them. He showed a precocious aptitude for all games employing logic and strategy. Given his first computer at age eight, he programmed it to play the board game Othello. His fascination with both games and computers grew with every year. By age 13, Hassabis was a recognized chessmaster and was soon playing adults in international competition. Although he enjoyed the intellectual stimulation of chess competition, he longed to apply his skills to a larger area and decided to pursue the study of artificial intelligence (AI). At 17, he joined the computer games company Bullfrog Productions, where he worked as a designer on the game Syndicate and was lead programmer for the highly influential Theme Park. A bestseller, it won the industry’s Golden Joystick Award and spawned a host of management simulation games.

Demis Hassabis started his professional games career at the age of 16 working at Bullfrog Productions. After finishing his bachelor’s in Computer Science Tripos, with a double degree from the Computer Lab at Cambridge, he joined the newly founded Lionhead Studios. In 1998, Hassabis founded Elixir Studios, producing games for Microsoft and Vivendi Universal, and contributed to many bestselling games, including Syndicate (1993), Theme Park (1994), Black & White (2001), Republic (2003), and Evil Genius (2004). Hassabis is an accomplished chess, shogi, and poker player, and has won the World Games Championships at the Mind Sports Olympiad (MSO) a record five times, prior to retiring in 2003.

Hassabis attended Cambridge University, where he led the chess team in 1995, and 1996, and again in 1997. Graduating that year with top honors in computer science, he joined Lionhead Studios, where he was the lead programmer for the game Black & White. The following year, he founded his own company, Elixir Studios. His first game as executive designer at Elixir was Republic: The Revolution, an innovative political simulation game. It was followed by the acclaimed super-villain simulator Evil Genius, and Hassabis concluded lucrative publishing deals with Vivendi and Microsoft. While running Elixir, Hassabis continued to participate in international games competition. In the London Mind Sports Olympiad, he emerged as the winner of the Pentamind World Championship in five consecutive years, from 1998 to 2003. In 2003 and in 2004, Hassabis won the championship in Decamentathlon. In 2004 he was the world team champion in the game Diplomacy. He was also a highly successful competitor in six different seasons of the World Series of Poker.

Having proven himself in world games competition and the computer games industry, Hassabis decided to study the working of the human brain to gain further insight into the possibilities of artificial intelligence. In 2005, he sold the intellectual property and technology rights to the games he had created and liquidated Elixir Studios. He began studies of cognitive neuroscience at University College London (UCL), completing his Ph.D. in 2009. He then traveled to the United States to pursue postdoctoral fellowships at Harvard and the Massachusetts Institute of Technology (MIT). He was a Wellcome Fellow at the UCL Neuroscience Unit.

March 9, 2016: Google DeepMind co-founder and chief scientist Demis Hassabis shakes hands with South Korean professional Go player Lee Se-dol before the Google DeepMind Challenge Match in Seoul, South Korea. Lee Se-dol played a five-game match against a computer program developed by Google, AlphaGo. AlphaGo is the first computer program to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history. AlphaGo’s landmark 4-1 victory in Seoul, South Korea was watched by over 200 million people worldwide. Before founding DeepMind, after a decade of experience leading successful technology startups, Hassabis returned to academia to complete a Ph.D. in cognitive neuroscience at University College London, followed by postdocs at MIT and Harvard University. (Photo: Google via Getty Images)

His doctoral studies focused on the field of autobiographical memory and amnesia. He has co-authored papers on the subject, published in Nature, Science, Neuron, and Proceedings of the National Academy of Sciences (PNAS). His paper in PNAS established for the first time that injury to the hippocampus area of the brain — which causes amnesia — also impairs the patient’s ability to imagine other situations. Hassabis had demonstrated the neurological connection between the functions of imagination and episodic memory — both require the ability to construct a scene mentally. His achievement was cited as one of the “Top 10 Scientific Breakthroughs of the Year” by the journal Science.

April 25, 2017: Demis Hassabis attends the 2017 TIME 100 Gala at Jazz at Lincoln Center in New York City. Hassabis was named one of TIME‘s 100 most influential pioneers, leaders, titans, artists, and icons of 2017. Ray Kurzweil, inventor, scientist, author, futurist, and director of engineering at Google, which owns DeepMind, wrote at the time, “Demis Hassabis is one of the leading scientists creating AI breakthroughs, with three Nature papers in the past two years. He believes, as I do, that AI will help solve humanity’s grand challenges—alleviating poverty, curing disease and improving the environment. Of equal importance, however, Demis is deeply committed to keeping AI safe. Every technology since fire has intertwined promise and peril. Demis has been a leader in establishing ethical guidelines to keep AI accountable. If we achieve this vision, it’s likely Demis will have played a large part.” (Getty)

In 2011, Hassabis founded the AI company DeepMind Technologies. He defined its mission as solving “the problem of intelligence” and then using artificial intelligence “to solve everything else.” Combining insights from neuroscience and machine learning with the latest developments in computer hardware, Hassabis is seeking to construct a mechanism for general-purpose learning — “artificial general intelligence” (AGI).

Hassabis and his DeepMind colleagues focused initially on creating learning algorithms to master games. By 2013, they had created an algorithm called Deep Q-Network (DQN) that could play computer games ”at a superhuman level.” With no input other than the pixels visible on the screen, and no directions other than “achieve the maximum score,” DQN became the world’s best player of Space Invaders within 30 minutes of its introduction to the game. DeepMind’s research caught the attention of technology giant Google, which purchased DeepMind for over $6500 million in 2014. Hassabis remains CEO of DeepMind, which operates as an independently managed entity, with headquarters in North London.

2017: Awards Council member and British cosmologist Lord Martin Rees presents the Golden Plate Award to Demis Hassabis, a British pioneer of artificial intelligence and a founder and CEO of DeepMind, at a ceremony in London.

Hassabis turned his attention to the challenge posed by the ancient Chinese game Go. The multiplicity of choices available to a Go player make it virtually impossible for even a master player of the game to explain completely the logic informing a given move. Hassabis regarded the game as an ideal challenge for a learning machine. In 2015, the DeepMind program AlphaGo defeated the European Go champion with a score of 5-0. The following year, it beat a former world champion, 4 -1.

DeepMind has also produced a “neural Turing machine,” that is, a recurrent neural network model combining the fuzzy pattern-matching capabilities of an artificial neural network with the algorithmic power of a programmable computer. The company’s progress in machine learning has married the processes of deep learning and reinforcement learning to create the new field of “deep reinforcement learning.” These processes hold enormous promise for almost every field of scientific study, from medicine to astrophysics. DeepMind is now collaborating with partners Moorfield’s Eye Hospital and Cancer Research UK Imperial Centre, who are using AI to analyze eye and mammography scans.

In 2015, the Financial Times listed Demis Hassabis as one of the “Top 50 Entrepreneurs in Europe” and the following year as the “Digital Entrepreneur of the Year.” Science magazine named AlphaGo one of the “Top 10 Scientific Breakthroughs of 2016” and TIME magazine listed Hassabis as one of the World’s 100 Most Influential People in 2017. In Britain’s 2018 New Year’s Honours, Demis Hassabis was named a Commander of the Order of the British Empire, and in May 2018, he was elected a member of the Royal Society, the world’s oldest scientific association.

The multi-talented Demis Hassabis is a widely-cited neuroscientist, pioneering artificial intelligence researcher, award-winning game designer, successful entrepreneur, and five-time World Games Champion. A child chess prodigy, he was coding bestselling computer games while still in his teens.

He led a series of successful technology startups before returning to academia to explore the application of neuroscience to artificial intelligence. His work demonstrating the link between the functions of memory and imagination was named one of the “Top Ten Scientific Breakthroughs of 2007” by the journal Science.

Hassabis is the founder and CEO of DeepMind, a neuroscience-inspired AI company, bought by Google in 2014 — their largest European acquisition to date. At DeepMind, he has fused the fields of machine learning with the insights of neuroscience to pursue the goal of “artificial general intelligence” — AGI — a general purpose learning technology with powerful implications for every area of scientific research. DeepMind’s mission, as Hassabis puts it, is “to solve the problem of intelligence and then use intelligence to solve everything else.”

We understand you were a competitive chess player from an early age. Do you remember when you first knew you were really good at it?

Demis Hassabis: Chess is something, like, I’ve always known how to do. I learned when I was four years old. I don’t even remember learning. I’ve been told often that I saw my dad and my uncle play a game of chess, and they were just normal amateur players. Apparently, I saw them playing and I asked to play. They thought, “Well, we’ll just…” you know, humor me and just teach me the rules. And then, a couple of weeks later, I was beating both of them, and then my dad thought, “Maybe we should take him to a chess club,” and it just sort of went from there, really. And then, from all my youth, I was always captain of my age group for the national team and usually playing with much older kids. So I sort of had the chess part of me as being a big core part of my upbringing and who I am.

What do you think you learned from those years of immersion in the game?

Demis Hassabis: I think chess — actually, a number of things that I learned when I was young — it wasn’t so much the chess itself; it was the skills that you were training while you were playing chess. So problem-solving, imagination, creative thinking, strategic thinking, all of these things — if you play chess to a high level, what you’re really honing is those meta-skills. And I realized from quite young, that’s one of the reasons I liked chess was this is a great practice for me to get good — sort of like a mental gym, if you like — to get really good at those other skills, which you could then translate to all sorts of other subjects like science or business.

At age 13, you were the second-best youth player in the world, and you decided to make a change. What were you thinking?

Demis Hassabis: I had a bit of an epiphany around that age because I was playing, you know —

I was more or less a professional chess player. I was going to school. I was taking weeks off school, to go around the world playing chess, and all my summer holidays and Christmas holidays and so on. So it was pretty full on. And the rest of the time, I was studying chess. And I remember thinking that to become world champion, which was my plan at that point, then your whole life would have to be dedicated to nothing but chess and learning opening moves and theory and all this kind of stuff. And I started realizing, the more I was specializing in chess, the less it was about these meta-skills that would be useful for other things; but more, that specific knowledge would be to just chess. So it wouldn’t be useful for other things you might want to do.

And I kind of had this thought when I was in a big tournament, actually, in Liechtenstein, near Switzerland, and I remember playing a big match. It was like a ten-hour match against — I think it was the Danish champion at the time, the adult champion. And I lost the match after ten, twelve hours. And I remember thinking, “Was that really a good use of all that brain power?”

And I went for a long walk in some beautiful field — at least I remember that in my mind — in the mountains. And I remember thinking, “Maybe this isn’t — there’s this whole room full of amazingly bright people, and they’re using their minds to basically compete with each other and try and win, and what was really — maybe they should all be using their minds to solve cancer or something.” And I was just sort of thinking, “Maybe you could harness all of these bright minds. Maybe there’s a better use of that time and energy that would be better for the world.” So I made this decision, actually, at that moment, that I would move away from chess and start exploring other areas and passions of mine and use those meta-skills as my foundations for getting to these other things. But I just felt — although I loved chess, and I still love chess now — I felt it was too narrow a thing to dedicate your entire life to.

March 8, 2016: (L-R) Demis Hassabis, CEO of Google’s artificial intelligence (AI) startup DeepMind, South Korean professional Go player Lee Se-dol, and Google CEO Eric Schmidt attend a press conference in Seoul, South Korea. Lee Se-dol is set to play a five-game match against a computer program developed by Google, AlphaGo. With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of the game Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-games competition, coined the DeepMind Challenge Match. Hundreds of millions of people around the world watched as legendary Go master Lee Se-dol took on an unproven AI challenger for the first time in history. (Photo credit: Kim Hee-Chul-Pool/Getty Images)

Demis Hassabis: So I dedicate my life to, you know, artificial intelligence — which is obviously what we do at DeepMind — but really, the underlying reason I work in artificial intelligence is I want to understand how the universe works. And I want to, you know — it’s the deeper scientific question. I want to try and answer the most deep questions that we have: What is consciousness? What are we doing here? What’s the universe about?

Do you have any early answers to what we’re doing here?

Demis Hassabis: I have some theories, but they’re not well-formed enough yet. I’m still working on them.

When I was a kid, I was actually — my favorite subject was physics. And usually, if you’re interested in the big questions about the universe, you would become a physicist. That’s the normal route for that. It was my favorite subject all the way up to university, but then I read this book called Dreams of a Final Theory by Steven Weinberg, who is a Nobel Prize winner, and I must have been in my late teens. And this is all about a physicist struggling to find a final theory of everything, like how everything works, and that’s what string theory’s about and so on. And I realized that they actually hadn’t got that far yet. Some of the things they were tackling were incredibly difficult, and no one seemed to be making progress. So it felt to me like maybe what was needed was more intelligence and maybe something like AI as a sort of smart tool to help scientists. It could be something that would allow us to make those kinds of breakthroughs and find the kind of theories that we were after. So it felt to me like working on AI would be more fruitful and then using that as a tool to help solve things in physics and other sciences.

What kind of breakthroughs might we see in the near future because of AI? What are you most excited about?

Demis Hassabis: The thing I’m most excited about is I think we’re on the cusp now of a new era in scientific discovery, where we’ll be able to use AI and machine learning to help us in areas of science and medicine, to make sense of enormous amounts of data that we’re generating from our experiments, and find new insights and new structure in that vast amount of data. I think that will lead to new scientific theories and new scientific breakthroughs.

How about medicine? Can you give us some examples of how AI could make us healthier?

Demis Hassabis: The obvious thing that’s already happening today has to do with radiography. So the low-hanging fruit, in some sense, is making sense of imaging data, whether that’s a PET scan of cancerous tissue or what we’ve worked directly on, which is retina scans and detecting macular degeneration very, very early on. There aren’t enough radiographers, and often the diagnosis from these scans is one of the bottlenecks to you getting treatment, and in a lot of these cases delayed treatment can be very critical to the outcome. So we’re already collaborating with many hospitals in the UK about adding in machine learning and AI tools to help with those pattern recognitions, if you like, those assessments about what’s happening in those scans. So I think that’s going to result in much more efficient diagnosis and much more efficient treatment. So I think that’s just the beginning. Then the next step will be things like drug discovery, where you’ve got some target, some virus or some bacteria, and you need to generate some new compound that will target that. I think, again there, we’re on the cusp of being able to use things like machine learning to discover new types of drugs.

How can a machine help us with drug discovery? Can you walk us through an example?

Demis Hassabis: What happens is, you know, if you think about — it’s a massive combinatorial problem. Like you’ve got lots and lots of possibilities. So you’ve got all these molecules that you could put together to create a new compound, which would create a new drug. And then you’ve got this complex new virus that you’ve just discovered, and you need to build a compound that will attach to it, right? So, really, the problem is that we know quite a lot about physics; we know a lot about chemistry and a lot about biology; but the combinations of possibilities is huge, right? It’s astronomical, the number of compounds you can combine together. And you would need to test that — each one — laboriously. Maybe you’d have some theories about things that could work, but you wouldn’t know for sure, as a scientist. What the kinds of systems we’re building — like AlphaGo and AlphaZero, our new systems — is you can think of them as optimization programs. You give them some goal to optimize, whether that’s efficacy with a drug or winning a game, and they figure out for themselves the right search path through this massive combinatorial space. And they do that by having to be able to experience many — look at much more data than any human could comprehend — and do that far more quickly. So these systems can find patterns that are there in the data that we, just as human scientists or human experts or human medical practitioners, wouldn’t have the bandwidth to do.

We know protein folding is key for Alzheimer’s research. Could you tell us a little more about that?

Demis Hassabis: Biologists would love to know — if you give them an amino acid sequence — they would love to know what the 3D structure of that ends up folding into because the 3D structure governs the protein’s functionality. So the problem is there’s no known theory or mapping for the 2D string of amino acid sequence to the 3D structure. So what we’d like to do is build a way of predicting the 3D structure just from the 2D amino acid sequence. And the way they do it at the moment is very laborious. You have to crystallize the protein, and then you can investigate its 3D structure, but it takes years for each protein and there’s millions of proteins. So we need to somehow speed that up.

So the goal is to figure out how proteins fold. And obviously, in Alzheimer’s, one of the hypotheses is that the amyloid beta protein is misfolding and not folding in the correct way, and they don’t know why. Something’s triggering that. So I imagine it could be helpful for that, down the line. I mean it wouldn’t directly solve Alzheimer’s, but it would be something you could use as a tool to help you speed up maybe trials for drugs for Alzheimer’s, but that would be for Alzheimer’s researchers to figure out. But we’re trying to build this sort of generic platform that many different biologists could use.

It sounds like you’re particularly interested in medicine. Is that fair to say about your goals for AI?

Demis Hassabis: We talked a lot about medicine, but I’m very interested in helping astronomers. Some people already use machine learning to discover new planets from data they have from their telescopes and find even smaller planets — exoplanets — that they couldn’t see or figure out from using their own maths. I’m interested in, you know — I’ve given a talk at CERN (European Center for Nuclear Research), and they now use machine learning to try and detect new particles. There are so many areas of science where — I mean science today is all about creating huge amounts of data from the experiments and then trying to make sense of that data. And that’s what AI does. AI — in a kind of most abstract sense — converts, you know, masses of data into actionable knowledge, right? So that’s got to be useful for all sorts of areas of science.

You’ve had such an extraordinary career. Could tell us about one “aha!” moment? Like one moment when you knew you’d found something.

Demis Hassabis: I’ve had quite a lot of “aha!” moments. I guess one of the really formative ones was when I first got my first computer. I was about eight years old. It was a ZX Spectrum. I remember starting to learn to program on it and sort of learning from books. I programmed some — one of my first programs I can remember was a program to play this game called Reversi or Othello. It’s called Othello in the UK, but it’s called Reversi, I think, in the U.S. And, you know, it played it reasonably well, and it could beat my kid brother. And I remember thinking, “This is amazing, that you can create a program that — you can go to sleep afterwards, and it can carry on number crunching — and then you can wake up the next day and it solved something for you that you wanted to know the answer to when you went to sleep.” So it seemed — again, I guess I was even then obsessed with efficiency — is that it seems incredible, like a way to enhance your mind, right? So, suddenly, you can outsource problems to this computer, and it can solve certain types of things for you. To me, that just seemed like magic and a magical extension of the mind. From then, I was sort of hooked on computers and programming and then, ultimately, AI.

Let’s talk some more about growing up. We gather you had an unusual childhood in some ways. What did your parents do?

Demis Hassabis: My parents, I always describe them as being a bit bohemian. I didn’t really realize that at the time when I was growing up. I just thought everybody was like this. But they didn’t really have proper set jobs, especially my father. He never really had a nine-to-five job, but he was always doing different things. He ran a toy store at one point. He was a singer-songwriter when he was younger. His favorite was Bob Dylan, so he wanted to sort of go into singing and songwriting. He still does that now, actually. And then he eventually became a teacher. But he did a whole host of different things, and he was very entrepreneurial. He always used to have, you know — the toy store was his own company. Even teaching now, his evening classes is his own small little company as well. So he was always doing his own things. I think he and my mom instilled in us that you could take whatever path you wanted in life and just follow your passions.

How did they do that?

Demis Hassabis: Well, just in terms of financial, we were a pretty poor family, but my father always used to make enough money, I think often by buying and selling houses. So we moved — I must have moved like ten times before I was around twelve years old. So we used to move house sort of every year, downsizing, upsizing, and just depending on the housing market, and that — I think he made a bit of money from that, and that was enough to keep us going.

That would have been unsettling for some kids.

Demis Hassabis: I just thought that was normal. I changed school lots of times, and I think, for a lot of kids, I realize now that would have been unsettling. But I think, for me, it just got me very used to settling in with new groups of people, making new friends, and getting on with things. I think that actually stood me in good stead in terms of running companies. I think that that way you have to interact with a lot of people. I think it probably has come from that being comfortable with lots of different environments.

A lot of your work is about saving time and being efficient. What’s your daily routine? How do you try to be efficient?

Demis Hassabis: I’m pretty obsessive about being efficient because I think time is so short, and life is so short, and there are so many interesting and amazing things to do and experience. So I don’t sleep very much. I’ve always been a nocturnal person. So I normally sleep at three or four in the morning, and I only have about five or six hours’ sleep, which is probably not enough, actually, as brain studies tell us, but there’s just so many things I need to pack into the day. So the way I normally split it is that, during the daytime, I manage the company and have meetings and do administrative things and organizational things, and then when I get home, I start a sort of second day of work — after dinner, around about 10 p.m. — where I start thinking about all my creative things. It’s when I read books or research papers, or write research papers, or come up with ideas about algorithms. I would do that in the small hours of the morning. I’ve always found myself at my best creatively in the small hours of the morning when everything is quiet.

And what do you think about in those quiet hours? Do you start with the big questions?

Demis Hassabis: Obviously, it depends on the mood and what I’ve got to do that day, but I generally think a lot about big questions in the late of night. It’s kind of conducive to that. How best to bring about these technologies? What’s missing from computers today? And then I dream about how we could deploy these things for the good of the world, and solve things like science, and which ordering of challenges should be tackled first.

Can you talk about one of the questions you’d like to tackle in the near future?

Demis Hassabis: We’re going to tackle protein folding. We’re already working on that, which is trying to discover the 3D structure of proteins just from the 2D amino acids structure.

We’re looking at a whole bunch of areas. We’re looking at quantum chemistry. We’re looking at theorem proving in mathematics. We’re looking at some areas of physics to do with quantum computing. There’s a wide range of areas that we are in the preliminary steps of.

Is there a particular talent or characteristic that you think accounts for your success?

Demis Hassabis: I think one reason that I think I’ve been successful in creating an unusual company like DeepMind is that I’ve been lucky in life, and I’ve been able to follow many passions that I’ve had. Initially, it was games. Then it was programming. Then it’s AI and neuroscience. I think I managed to build a world or a career that utilizes all of those interests, and I can sort of indulge in all those passions as part of my job.

But the actual one that I have a passion for, which is sort of tangential to those things, is organizational design. I don’t know why that is because it’s quite different. It’s probably connected to my obsession with efficiency, where a company is really — obviously — they’re made of groups of individuals. And so how you organize them and what kind of culture you have will determine how much output that organization could produce.

I think, in my career, I’ve been lucky to have worked with many, many brilliant, brilliant people and, of course, including at DeepMind. I’ve learned, I think, how to try and get the best out of those people and what sort of cultures that requires. I think that’s led to DeepMind having quite a unique culture. And I think that’s just as big an effect on the output we have as our research ideas.

How many Ph.D.’s do you have here at DeepMind headquarters?

Demis Hassabis: We have over 400 Ph.D.’s just on these two floors. I think it’s the biggest collection anywhere in the world of brainpower, on this topic, that’s ever been collected.

What is your short-term goal to do with all that brainpower?

Demis Hassabis: The goal is to make as fast progress as possible towards building human-level AI. And then the idea is that, as we build these technologies, we’ll be able to apply them usefully for the benefit of the world in all sorts of sectors like healthcare and science. So I think we — in a way, you can think of AI as a kind of meta-solution to all of the other problems that we would like to have solutions to, as a society.

The prospect of human-level AI is very exciting, but some people find it very unsettling. There could be negative consequences as well as positive ones. Where do you fall on that question?

Demis Hassabis: I’m in-between on those camps, actually. Obviously, we work on AI, and I’ve spent my whole career on it because I think it’s going to be one of the most amazing empowering technologies for humanity ever invented. I think of it as a smart tool that will unleash the true potential of human ingenuity by sort of working — for us being able to draw on these kinds of smart tools to help us, as I mentioned, in science and medicine.

But there’s going to be a lot of disruption, too, like with any new powerful technology, and AI might be one of the most disruptive of all. We’ve always been cognizant of that, from the start of DeepMind and even before, and we’ve always thought about ethics and responsibility of stewarding that kind of powerful technology into the world as being a really huge responsibility that we have as one of the leading exponents of this technology.

We’ve been at the forefront of leading the debate on that, where things — we brought together a partnership on AI, which is a big consortium of all the biggest companies coming together to think about the best way to deploy this kind of technology. We’ve always been thinking about the ethics of this from the beginning. So we believe that, stewarded correctly, this technology should be for the benefit of everyone.

I think we’re going to see some amazing breakthroughs that I think, frankly, society needs — from climate change to Alzheimer’s — where we’re not making enough progress as a society on these kinds of very, very pressing problems. I think if we could do science better and we had more intellectual horsepower behind it, we could make better, faster solutions.

How can you deploy AI to help us manage climate change?

Demis Hassabis: There are so many ways. You could invent some new, much more efficient, renewable energy. You could maybe solve fusion. You could try and create a new material that would capture carbon cheaply out of the atmosphere. The sky’s the limit really, in terms of if you have that kind of optimization technique in a box that you can call upon and have it present you with alternatives.

You could give it a goal function. We could specify a goal clearly to it, some metric, which, I think you probably could with something like climate. I think that you could really make progress with new materials or new solutions to those problems. Another way that it could help with climate is you could do better modeling of the climate so predictions could be more accurate about what’s happening.

You could analyze satellite imagery in real time so that you could see if icebergs were breaking up, and you could see if deforestation was happening where it wasn’t supposed to be happening. I think NGOs — we talked to a lot of NGOs about this — there might be international agreements, but how do you track that on the ground when there’s such vast amounts of space going on? So I think there’s multiple ways, actually, on a number of levels, that AI could help with something like climate.

Will there be any work left for humans to do?

Demis Hassabis: I think there’s many things left. There are all the creative pursuits. There’s also all the caregiving jobs. I make this joke sometimes that, I think, is semi-serious. Let’s take healthcare. We pay doctors a lot more than nurses, as a society. In the future, in ten, twenty years’ time, I could imagine the doctor that doesn’t have some kind of AI database that they’re using to keep them up to speed with the latest developments, that’s not a doctor you’d want to see. So diagnosis, I imagine, is fairly logical. It could be enhanced by AI. But on the other hand, if you’re ill in the hospital, and you want to be looked after, I think a nurse and a human touch would be a lot better than having a robot. So I feel like those kinds of jobs, like teaching and other things that maybe society undervalues currently, will become more valued. So I can imagine, one day, maybe nurses will be paid more than doctors, and maybe that’s a good thing. At the moment, we value certain things in society, and maybe those things will become less rare and other things will become more rare and more unique.

Is there anything a robot is not going to be able to do?

Demis Hassabis: Well, right now robots can do anything basically. That’s an interesting philosophical question. This is where neuroscience comes in. What is the brain? Is it just the physical biology or is that something more? And from neuroscience, it looks that — all the neuroscience discovery so far — it looks like you could describe the brain in mechanistic terms. And if that’s true, then most of the functions that the brain has, you should be able to mimic in some way with a computer. This is what Alan Turing was studying. He’s one of my all‑time heroes, and he came up with this idea of a Turing machine, which is a machine that can mimic any other machine. And we know the human brain is a Turing type of machine. That’s how Turing was able to come up with that idea. It remains to be seen whether AIs can do all of those different capabilities.

Let’s go back to the question of time management. Is there something that a lot of ordinary humans are doing that you see as not really time-efficient?

Demis Hassabis: This goes back to the jobs question. You and I are lucky. We’re doing jobs we love, but there’s many people in the world who are doing jobs because they have to, because they need to earn enough money to pay for their families and so on. Actually, one question I would always ask is that, if you were to win the lottery tomorrow, how many people would actually go back to their jobs? I’m sure that study’s been done by Harvard Business School or something. But I suspect it’s a very small fraction, like less than five percent. Maybe less than one percent of the world would do that. So that shows to me that a lot of people are spending many, many hours doing things they are not passionate about and not interested in and possibly quite mundane. So maybe it would be good if we can relieve people from that drudgery and yet still make sure that they share in the productivity gains and the prosperity that that new productivity will create.

If all these jobs are mechanized, will there be enough jobs left that people can actually earn a living?

Demis Hassabis: This has happened before many times. The Industrial Revolution did that. We don’t spin wheels in weaving houses anymore. They went on to be, I guess, designers and fashion designers or something. So this is nothing new. There’s always been an evolution where new technologies come in, so that makes some jobs more efficient. So there’s some disruption, and then it opens up new jobs that we can’t even imagine today. I think it’s going to be the same.

Really exciting things have happened with machines being able to translate the spoken word. Do you see a time when there’s some kind of machine that can automatically translate so that we won’t have to learn languages anymore?

Demis Hassabis: I’m sure there will be some sort of device that allows you to get it on a rudimentary level. But I think it still won’t — like currently, to learn languages, if you really want to understand the nuances of a great novel, you need to read it in the original to get all of the finer details and nuances of what the writer was getting at; I suspect that will be true still for a long while, but just basic functional level translation, yeah, sure. I think we’re almost there already with the translation systems that we have. Of course, they don’t really understand language at all. They’re just doing statistical correlations between the two languages. So it’s funny, they can do translation, but they don’t understand language. They can’t create language. They can just do a sort of mapping from one language to the other.

Is there ever going to be a machine that can write a play or sing a song?

Demis Hassabis: I think we’ll have machines that can write things that are coherent English. But will they be a great novel? I doubt it because I was having this debate, actually, with somebody — a famous writer — and we were talking about this exact topic. My view is that a great novel is also partly because you know the person who has written it was human, and they went through some things, and you can connect to the human condition that they’re describing. I think that’s important for a novel to be great. I don’t think it’s just the technical grammatical components of the novel.

I think it’s also the fact that it was written by another human that you can empathize with. That’s the reason why, I think, we bother to say, for some stories, “This is based on a true story.” Why would we say that unless, for some reason, that makes it more emotive, that we know somebody else has gone through that — actually gone through that — in real life? I think there’s an element of that. I don’t know if it would be as meaningful if you knew that it was written by a machine.

Is there anything in this amazing power that is being unleashed by AI that you’re a little bit worried about?

Demis Hassabis: It’s like any powerful technology. The technology is neutral in and of itself. It depends on how humans are going to use it. All of the history of science has been like that from nuclear to biology. There are many good and bad ways to use these technologies. We just have to make sure that we build AI in a safe way, in a controllable way, and I see that as like a tool. And then the second question is making sure you deploy it for the good of the world rather than the bad.

How is this going to change warfare? Will there just be a future of weaponized drones and robots fighting each other?

Demis Hassabis: I’m against using any of this kind of technology for any kind of military. That’s not something I’m going to get involved with or we would do at DeepMind. I can’t speak for what other organizations will do. My view is that — and we’ve signed open letters and UN letters to this effect — that there should always be humans in the loop for any kind of warfare or weapons system.

Another thing people are worried about is the prospect of an AI arms race. At the moment, the U.S. is more advanced than China in this area, so China is putting a lot of focus and money into it. Is there an AI arms race going on?

Demis Hassabis: I wouldn’t say so. I think that, on the research front, the researchers know each other pretty well and go to the same conferences, and there’s a spirit of information. For example, we publish all of our work in big journals like Nature and Science. So I don’t think there’s a sort of classical arms race. There’s definitely a competition in terms of being the best commercially and trying to get the biggest benefit out of these technologies, but I wouldn’t say it’s a classical arms race. No.

Do you think there’s enough focus by governments on the need to fund and focus on AI, given that they can harness such power?

Demis Hassabis: I think there’s probably enough focus. I think there needs to be a better understanding, probably, in government of what AI is. We certainly help talk to various people that we know in the UK, to try and educate them about what these technologies are because, as you said, they are quite hard to understand. I think it’s definitely something that needs to be debated. And again, as mentioned earlier, we are at the forefront of having these ethical debates and bringing that into light, about how do we want to use these systems and deploy these systems. For example, not use them for war.

Returning to some of the really fundamental questions, what do you think is the role of human beings in the universe?

Demis Hassabis: I think humans occupy a very special place in the universe. As far as we know, we haven’t discovered any alien life. So we’re the only sort of fully conscious beings in the universe, and I think that’s very key. We have to figure out what consciousness is, but I think it was Carl Sagan that said we’re “waking up the universe.” The universe, it’s huge, but it doesn’t have any self-awareness and that’s what we bring to the universe. So I think humans have an extraordinary, unique part in the universe from that perspective.

Do you think we were created for this purpose? Do you think the universe was a work of conscious creation?

Demis Hassabis: I’m open-minded about these things. And what you know, as a scientist, actually, is that — if you’re a proper scientist — of course, science, the scientific method, is one of the greatest inventions mankind has ever had, that we’ve ever had as humanity. It worked brilliantly, the scientific method, and that is what has allowed us to have the Enlightenment and modern civilization.

But I think, as a scientist, what you realize is, the more you find out, actually, the bigger questions there are unresolved. You sort of go deeper and then there’s more things you don’t know. In physics, for example, you know, we don’t know what dark matter is and dark energy now. And there’s theories about it, but we don’t really know. I think there’s always more questions. Like, there’s the Big Bang, so we think that’s how the universe started, but what was before the Big Bang?

So I’m quite open-minded to the fact that there may be many aspects of the physical universe that we just don’t understand and that, when we have maybe something like AI helping us, we will get a truer understanding of what’s going on here. And then we’ll be better able to answer the questions about what the purpose of the universe was, and was it designed, and how did it come about, much better.

Is there anything in science that’s too difficult to look at?

Demis Hassabis: No. If you can’t specify the objective that you’re after very clearly, then it’s difficult for these optimization systems to make progress towards it. Also, they need a lot of data — or simulation — to get enough simulated data to learn from. So they still — at the moment, the systems aren’t that efficient in the amount of data they use. They still need vast amounts of data. And in a lot of subject areas, you just don’t have that much data.

Is that the key thing? Identifying the goal?

Demis Hassabis: Yeah. Identifying the goal is one of the very key things, certainly in the way we build our AI systems, where it’s called “reinforcement learning,” where they learn incrementally to progress towards their goal. There are examples in games where their goal is to win the game, and they figure out what are the different ways that are helpful towards winning the game. But they figure that part out themselves. So specifying the goal is incredibly important. Having a metric to tell you how close you are to the goal so you know that you’re heading in the right direction is important and having simulation or a lot of ground truth data that you can learn from. So those are kind of the three components.

Is there anything special you do to spur your creativity?

Demis Hassabis: Yeah. I use music a lot, actually. So when I’m focusing, I’ll listen to music, different music depending on what mood I’m trying to create. I like, actually, everything from classical to “drum and bass.” It just depends on what I’m trying to do. So if I’m programming, it would be something with a beat or just more energetic. Whereas, if I’m thinking or reading, maybe it will be classical, something more like Vivaldi or Mozart, something to get you in the right mood. So it’s very much to do with getting you into the right kind of zone, and I’ve learned a lot about myself. This is something I recommend young people to do is — learn about how they work best is one of the most important things — and what triggers you into certain creative states and then maximize that.

The other thing I use is I really like film. So that’s one of the main things I do to relax is I love watching great films, and I find them very inspirational. And then once I’ve watched a great film, I can usually retrigger that mood or that emotion just by watching a small clip on YouTube of that film, and I can pretty much evoke the whole of that emotional state, reconstruct that for whatever it is I’m doing in that moment.

Let’s say you have a deadline and you really are thinking hard. What music do you put on?

Demis Hassabis: It would probably be some kind of — I think it’s called “liquid drum and bass,” actually — it’s actually a subgenre of drum and bass. So it’s got a beat, but it’s got melody as well, but it has to be interesting. While I have to focus, it can’t have words because then your brain starts listening to the words. So I don’t have any lyrics. I can’t listen to anything that has lyrics. It has to be just instrumental. And then, I think, probably what’s happening — in neuroscience, I think there’s been some studies about this — is it gets you into a kind of alpha state because this type of modern music is quite repetitive. And once you’ve heard it a few times, it’s almost in the background, and I think it’s just getting your brain into some kind of rhythm, which I find better for focusing.

Here at the Google DeepMind headquarters in London, you have a music room. Do people really use it?

Demis Hassabis: Yeah. We have a DeepMind band. They’re very good. The thing we have here is we have a lot of people who have hidden talents in many domains. So they’re all like brilliant scientists and engineers. But often, in growing up, they’ll have hidden talents, like they’ll be, you know, concert pianists on the side or some are writers on the side. We have, actually, a very talented house band that gets together and plays at all of our Christmas parties and other things. So I think they use the music room mostly for jam sessions.

What’s one of your favorite movies?

Demis Hassabis: I would say many, but the most influential to me was probably Blade Runner, the original Blade Runner. That obviously was for obvious reasons because it’s to do with artificial intelligence and the nature of being human. But I have many. It depends on, again, the mood I’m in, what film I would go to.

If you can build a machine that can literally read every book in the world, does it still matter for a human being to be well read?

Demis Hassabis: Of course it does. These systems, they’re just machines like cars and planes. They extend the human capability, but the human at the heart of it still has to be educated and soulful and is still providing the kind of emotional impetus to these tools. These tools will be nothing without humans.

Should we adjust what we teach students in school and universities because of the coming changes?

Demis Hassabis: I don’t think this is just to do with AI. I think this is also just to do with technology in general. You look at the Internet and mobile, how fast things have changed. And just going back to the jobs questions, there are jobs that exist today that you couldn’t have imagined twenty years ago, being a YouTube content creator or certain types of game designers, other things like that.

There’s this whole new class of jobs that’s come up. And I think that will happen again, enabled by all the new technologies, of which AI is one. So what I would recommend to students today is that, rather than focus on jobs for life, like, “I’m going to be X — an accountant — and that’s going to be 50 years, and I’m done, so I’ll just train that,” I think it’d be better to learn general skills that you could then adapt to any new type of sector that you’ve got to learn about. So that’s things like learning to learn. So what I would encourage schools to teach is not specifically so much the rote learning or the specific subject or anything but actually the ability to learn, if that makes sense.

So how do you learn best? What makes you learn best? How can you accelerate your own learning no matter what subject you’re learning? So it’s subject independent, but it’s just the ability to learn, and that’s something that isn’t really taught at schools, are these metacognitive skills that will be useful no matter which way the world goes. Because I think the world’s becoming unpredictable, and we don’t know what new technology is going to come out, and things are changing very fast. I think, in that type of environment, you want to be as adaptable as possible.

For young people who are really excited about all the possibilities that AI is bringing, what advice do you have for them? They have their whole life in front of them. What should they be doing now?

Demis Hassabis: I think they should learn how to get the best for themselves and what they’re passionate about. And I think they should learn skills that are transferrable — so, metacognitive skills that will stand them in good stead no matter which way technology goes or which way the world goes. They can find the most valuable things that make the most impact and make a difference in those areas if they have that kind of generalized training, rather than subject-specific training, which has been the sort of history of schooling in the last century or so. And I would say it’s a really exciting moment in human history, that we’re standing on the cusp of some really amazing things happening, where I think, if it goes well, life will be way, way better for the vast majority of the world.

Do you have any suggestions for people who want to accelerate their learning process?

Demis Hassabis: Well, I think the first thing is, you’ve got to know yourself really well. That’s what I did when I was a kid, is, like, how do you learn best? What motivates you? What are you passionate about? What hours of the day do you work best on? These are all the things that you are never really taught at school and actually are the most important things — are how to get the best out of yourself. And then I would say, once you’ve mastered that, then you should think about building on your strengths and passions to create a unique combination of skills that makes you unique compared to everyone else and develop those strongly. So I think that’s what I’d recommend, and I think a lot of the big breakthroughs and new companies that are going to be created in the future are interdisciplinary ones, where you make connections between two disparate subjects, and you are the only one who can really see the connection between those two subjects. And I think that’s going to happen again and again in the next ten to twenty years. I think it’s going to be where a lot of the big breakthroughs come from.

And what motivates you?

Demis Hassabis: So, what motivates me is trying to be the best I can and, you know, do justice to sort of the kind of upbringing I had and the talents I had when I was young and see to make the best use of that. But really, the thing that I’m most passionate about is understanding the world. So I would like to understand the big questions, like what’s consciousness, what’s going on in our brains, and what’s going on out in the universe. So that’s why I’m interested in physics and neuroscience. And I hope to use AI to help advance both those two fields.